Prediction of Crime Occurrence using Multinomial Logistic Regression
R. Rajadevir1, E. M. Roopa Devir2, S. Vinoth Kumar3
1R. Rajadevi*, Department of Information Technology, Kongu Engineering College, Perundurai, India.
2E. M. Roopa Devi, Department of Information Technology, Kongu Engineering College, Perundurai, India.
3S. Vinoth Kumar, Department of Information Technology, Kongu Engineering College, Perundurai, India.
Manuscript received on December 15, 2019. | Revised Manuscript received on December 21, 2019. | Manuscript published on January 10, 2020. | PP: 1432-1435 | Volume-9 Issue-3, January 2020. | Retrieval Number: B7663129219/2020©BEIESP | DOI: 10.35940/ijitee.B7663.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In order to uncover hidden patterns and correlations, data analysis examines large amounts of data. Analysis of crime isa systematic approach to the identification and analysis of crime patterns and itstrends. This plays a role in the planning of problems with crime and in formulating strategies for crime prevention. Instead of focusing on causes of crime such as criminal offender background, this work focuses primarily crime factors happened on every day. This work can predict the category of crime that has a higher likelihood of occurrence in those areas and can visualize in the form of histogram and heat map by category of crime, crime by day of week and month. The study depends on a lot of variables like class, latitude, longitude, etc. For forecast, the multinomial logistic regression method is used. For weekdays, the district and the hour of the accident are used as predictors.This algorithm is used because its target variable has more than two values and no ordering in the response variable.This provides greater efficiency for handling datasets with multi class labels. This forecast can be helpful in predicting the occurrence of crime in vulnerable areas, which in turn minimizes the crime rate by providing the patrol in those areas.
Keywords: Data Analytics, Prediction, Regression ,Machine Learning.
Scope of the Article: Machine Learning